Overview

Dataset statistics

Number of variables26
Number of observations12679
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 MiB
Average record size in memory257.7 B

Variable types

Numeric20
Categorical6

Alerts

date has a high cardinality: 1287 distinct valuesHigh cardinality
home_name has a high cardinality: 146 distinct valuesHigh cardinality
away_name has a high cardinality: 146 distinct valuesHigh cardinality
gameID is highly overall correlated with seasonHigh correlation
season is highly overall correlated with gameIDHigh correlation
homeTeamID is highly overall correlated with awayTeamID and 1 other fieldsHigh correlation
awayTeamID is highly overall correlated with homeTeamID and 1 other fieldsHigh correlation
homeGoals is highly overall correlated with xGoals_home and 1 other fieldsHigh correlation
awayGoals is highly overall correlated with xGoals_away and 1 other fieldsHigh correlation
xGoals_home is highly overall correlated with homeGoals and 2 other fieldsHigh correlation
shots_home is highly overall correlated with xGoals_home and 3 other fieldsHigh correlation
shotsOnTarget_home is highly overall correlated with homeGoals and 2 other fieldsHigh correlation
deep_home is highly overall correlated with shots_homeHigh correlation
corners_home is highly overall correlated with shots_homeHigh correlation
xGoals_away is highly overall correlated with awayGoals and 2 other fieldsHigh correlation
shots_away is highly overall correlated with xGoals_away and 2 other fieldsHigh correlation
shotsOnTarget_away is highly overall correlated with awayGoals and 2 other fieldsHigh correlation
deep_away is highly overall correlated with shots_awayHigh correlation
result_away is highly overall correlated with result_homeHigh correlation
result_home is highly overall correlated with result_awayHigh correlation
liga is highly overall correlated with homeTeamID and 1 other fieldsHigh correlation
gameID has unique valuesUnique
homeGoals has 2942 (23.2%) zerosZeros
awayGoals has 4030 (31.8%) zerosZeros
shotsOnTarget_home has 228 (1.8%) zerosZeros
deep_home has 230 (1.8%) zerosZeros
corners_home has 187 (1.5%) zerosZeros
shotsOnTarget_away has 431 (3.4%) zerosZeros
deep_away has 475 (3.7%) zerosZeros
corners_away has 356 (2.8%) zerosZeros

Reproduction

Analysis started2023-02-23 23:07:28.026606
Analysis finished2023-02-23 23:08:11.424058
Duration43.4 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

gameID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12679
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7877.6278
Minimum81
Maximum16135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:11.503077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum81
5-th percentile714.9
Q13812.5
median7981
Q312023.5
95-th percentile15501.1
Maximum16135
Range16054
Interquartile range (IQR)8211

Descriptive statistics

Standard deviation4753.9531
Coefficient of variation (CV)0.60347521
Kurtosis-1.1924522
Mean7877.6278
Median Absolute Deviation (MAD)4106
Skewness0.069888227
Sum99880443
Variance22600070
MonotonicityNot monotonic
2023-02-23T20:08:11.626104image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 1
 
< 0.1%
5688 1
 
< 0.1%
5690 1
 
< 0.1%
5691 1
 
< 0.1%
5692 1
 
< 0.1%
5693 1
 
< 0.1%
5694 1
 
< 0.1%
5695 1
 
< 0.1%
5696 1
 
< 0.1%
5697 1
 
< 0.1%
Other values (12669) 12669
99.9%
ValueCountFrequency (%)
81 1
< 0.1%
82 1
< 0.1%
83 1
< 0.1%
84 1
< 0.1%
85 1
< 0.1%
86 1
< 0.1%
87 1
< 0.1%
88 1
< 0.1%
89 1
< 0.1%
90 1
< 0.1%
ValueCountFrequency (%)
16135 1
< 0.1%
16134 1
< 0.1%
16133 1
< 0.1%
16132 1
< 0.1%
16131 1
< 0.1%
16130 1
< 0.1%
16129 1
< 0.1%
16128 1
< 0.1%
16127 1
< 0.1%
16126 1
< 0.1%

season
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.9841
Minimum2014
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:11.722126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12015
median2017
Q32019
95-th percentile2020
Maximum2020
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0001541
Coefficient of variation (CV)0.00099165582
Kurtosis-1.2439111
Mean2016.9841
Median Absolute Deviation (MAD)2
Skewness0.015825782
Sum25573342
Variance4.0006163
MonotonicityNot monotonic
2023-02-23T20:08:11.792141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2015 1826
14.4%
2014 1826
14.4%
2018 1826
14.4%
2020 1826
14.4%
2016 1825
14.4%
2017 1825
14.4%
2019 1725
13.6%
ValueCountFrequency (%)
2014 1826
14.4%
2015 1826
14.4%
2016 1825
14.4%
2017 1825
14.4%
2018 1826
14.4%
2019 1725
13.6%
2020 1826
14.4%
ValueCountFrequency (%)
2020 1826
14.4%
2019 1725
13.6%
2018 1826
14.4%
2017 1825
14.4%
2016 1825
14.4%
2015 1826
14.4%
2014 1826
14.4%

date
Categorical

Distinct1287
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size198.1 KiB
2018-12-22
 
37
2015-04-04
 
34
2017-04-15
 
34
2015-05-23
 
31
2021-05-16
 
31
Other values (1282)
12512 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters126790
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)1.3%

Sample

1st row2015-08-08
2nd row2015-08-08
3rd row2015-08-08
4th row2015-08-08
5th row2015-08-08

Common Values

ValueCountFrequency (%)
2018-12-22 37
 
0.3%
2015-04-04 34
 
0.3%
2017-04-15 34
 
0.3%
2015-05-23 31
 
0.2%
2021-05-16 31
 
0.2%
2019-12-21 30
 
0.2%
2018-03-31 30
 
0.2%
2019-05-18 30
 
0.2%
2020-12-16 29
 
0.2%
2019-04-20 29
 
0.2%
Other values (1277) 12364
97.5%

Length

2023-02-23T20:08:11.881162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-12-22 37
 
0.3%
2017-04-15 34
 
0.3%
2015-04-04 34
 
0.3%
2015-05-23 31
 
0.2%
2021-05-16 31
 
0.2%
2019-12-21 30
 
0.2%
2018-03-31 30
 
0.2%
2019-05-18 30
 
0.2%
2018-05-12 29
 
0.2%
2019-05-12 29
 
0.2%
Other values (1277) 12364
97.5%

Most occurring characters

ValueCountFrequency (%)
0 29162
23.0%
- 25358
20.0%
2 24023
18.9%
1 23018
18.2%
9 4359
 
3.4%
5 4030
 
3.2%
8 3804
 
3.0%
4 3528
 
2.8%
7 3257
 
2.6%
6 3216
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101432
80.0%
Dash Punctuation 25358
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29162
28.8%
2 24023
23.7%
1 23018
22.7%
9 4359
 
4.3%
5 4030
 
4.0%
8 3804
 
3.8%
4 3528
 
3.5%
7 3257
 
3.2%
6 3216
 
3.2%
3 3035
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 25358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126790
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29162
23.0%
- 25358
20.0%
2 24023
18.9%
1 23018
18.2%
9 4359
 
3.4%
5 4030
 
3.2%
8 3804
 
3.0%
4 3528
 
2.8%
7 3257
 
2.6%
6 3216
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126790
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29162
23.0%
- 25358
20.0%
2 24023
18.9%
1 23018
18.2%
9 4359
 
3.4%
5 4030
 
3.2%
8 3804
 
3.0%
4 3528
 
2.8%
7 3257
 
2.6%
6 3216
 
2.5%

homeTeamID
Real number (ℝ)

Distinct146
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.3225
Minimum71
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:11.973658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile76
Q1101
median132
Q3163
95-th percentile225
Maximum262
Range191
Interquartile range (IQR)62

Descriptive statistics

Standard deviation42.758873
Coefficient of variation (CV)0.31597755
Kurtosis-0.08680446
Mean135.3225
Median Absolute Deviation (MAD)31
Skewness0.63136843
Sum1715754
Variance1828.3212
MonotonicityNot monotonic
2023-02-23T20:08:12.077681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89 133
 
1.0%
102 133
 
1.0%
156 133
 
1.0%
150 133
 
1.0%
152 133
 
1.0%
140 133
 
1.0%
154 133
 
1.0%
147 133
 
1.0%
96 133
 
1.0%
98 133
 
1.0%
Other values (136) 11349
89.5%
ValueCountFrequency (%)
71 76
0.6%
72 133
1.0%
73 95
0.7%
74 133
1.0%
75 133
1.0%
76 95
0.7%
77 57
0.4%
78 133
1.0%
79 38
 
0.3%
80 133
1.0%
ValueCountFrequency (%)
262 17
0.1%
261 19
0.1%
260 19
0.1%
245 19
0.1%
243 19
0.1%
242 19
0.1%
241 33
0.3%
240 34
0.3%
239 19
0.1%
238 38
0.3%

awayTeamID
Real number (ℝ)

Distinct146
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.33047
Minimum71
Maximum262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:12.192707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile76
Q1101
median132
Q3163
95-th percentile225
Maximum262
Range191
Interquartile range (IQR)62

Descriptive statistics

Standard deviation42.768069
Coefficient of variation (CV)0.3160269
Kurtosis-0.08810798
Mean135.33047
Median Absolute Deviation (MAD)31
Skewness0.63125208
Sum1715855
Variance1829.1077
MonotonicityNot monotonic
2023-02-23T20:08:12.296733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 133
 
1.0%
138 133
 
1.0%
156 133
 
1.0%
154 133
 
1.0%
152 133
 
1.0%
148 133
 
1.0%
146 133
 
1.0%
95 133
 
1.0%
140 133
 
1.0%
99 133
 
1.0%
Other values (136) 11349
89.5%
ValueCountFrequency (%)
71 76
0.6%
72 133
1.0%
73 95
0.7%
74 133
1.0%
75 133
1.0%
76 95
0.7%
77 57
0.4%
78 133
1.0%
79 38
 
0.3%
80 133
1.0%
ValueCountFrequency (%)
262 17
0.1%
261 19
0.1%
260 19
0.1%
245 19
0.1%
243 19
0.1%
242 19
0.1%
241 33
0.3%
240 34
0.3%
239 19
0.1%
238 38
0.3%

homeGoals
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5333228
Minimum0
Maximum10
Zeros2942
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:12.392756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.306713
Coefficient of variation (CV)0.85220999
Kurtosis1.3422552
Mean1.5333228
Median Absolute Deviation (MAD)1
Skewness0.99088635
Sum19441
Variance1.7074989
MonotonicityNot monotonic
2023-02-23T20:08:12.464772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 4086
32.2%
2 3099
24.4%
0 2942
23.2%
3 1571
 
12.4%
4 620
 
4.9%
5 249
 
2.0%
6 83
 
0.7%
7 16
 
0.1%
8 9
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 2942
23.2%
1 4086
32.2%
2 3099
24.4%
3 1571
 
12.4%
4 620
 
4.9%
5 249
 
2.0%
6 83
 
0.7%
7 16
 
0.1%
8 9
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 3
 
< 0.1%
8 9
 
0.1%
7 16
 
0.1%
6 83
 
0.7%
5 249
 
2.0%
4 620
 
4.9%
3 1571
 
12.4%
2 3099
24.4%
1 4086
32.2%

awayGoals
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2067198
Minimum0
Maximum9
Zeros4030
Zeros (%)31.8%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:12.544790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1677148
Coefficient of variation (CV)0.96767689
Kurtosis1.645067
Mean1.2067198
Median Absolute Deviation (MAD)1
Skewness1.1223221
Sum15300
Variance1.3635579
MonotonicityNot monotonic
2023-02-23T20:08:12.612809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 4458
35.2%
0 4030
31.8%
2 2522
19.9%
3 1108
 
8.7%
4 395
 
3.1%
5 122
 
1.0%
6 31
 
0.2%
7 8
 
0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
0 4030
31.8%
1 4458
35.2%
2 2522
19.9%
3 1108
 
8.7%
4 395
 
3.1%
5 122
 
1.0%
6 31
 
0.2%
7 8
 
0.1%
8 3
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 3
 
< 0.1%
7 8
 
0.1%
6 31
 
0.2%
5 122
 
1.0%
4 395
 
3.1%
3 1108
 
8.7%
2 2522
19.9%
1 4458
35.2%
0 4030
31.8%

home_club_id
Real number (ℝ)

Distinct146
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1208.8102
Minimum3
Maximum23826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:12.704834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q1148
median533
Q31041
95-th percentile4171
Maximum23826
Range23823
Interquartile range (IQR)893

Descriptive statistics

Standard deviation2748.488
Coefficient of variation (CV)2.2737135
Kurtosis37.571993
Mean1208.8102
Median Absolute Deviation (MAD)460
Skewness5.6862513
Sum15326504
Variance7554186.5
MonotonicityNot monotonic
2023-02-23T20:08:12.810857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
985 133
 
1.0%
1038 133
 
1.0%
1533 133
 
1.0%
418 133
 
1.0%
940 133
 
1.0%
681 133
 
1.0%
1050 133
 
1.0%
621 133
 
1.0%
398 133
 
1.0%
506 133
 
1.0%
Other values (136) 11349
89.5%
ValueCountFrequency (%)
3 102
0.8%
4 17
 
0.1%
5 133
1.0%
10 17
 
0.1%
11 133
1.0%
12 133
1.0%
13 133
1.0%
15 119
0.9%
16 119
0.9%
18 119
0.9%
ValueCountFrequency (%)
23826 85
0.7%
16795 95
0.7%
14171 19
 
0.1%
12321 38
 
0.3%
8970 38
 
0.3%
6574 133
1.0%
6195 133
1.0%
5358 38
 
0.3%
4795 34
 
0.3%
4171 38
 
0.3%

away_club_id
Real number (ℝ)

Distinct146
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1208.6161
Minimum3
Maximum23826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:12.919887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q1148
median533
Q31041
95-th percentile4171
Maximum23826
Range23823
Interquartile range (IQR)893

Descriptive statistics

Standard deviation2748.521
Coefficient of variation (CV)2.274106
Kurtosis37.571659
Mean1208.6161
Median Absolute Deviation (MAD)460
Skewness5.6862478
Sum15324043
Variance7554367.5
MonotonicityNot monotonic
2023-02-23T20:08:13.026911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148 133
 
1.0%
368 133
 
1.0%
1533 133
 
1.0%
1050 133
 
1.0%
940 133
 
1.0%
131 133
 
1.0%
1049 133
 
1.0%
12 133
 
1.0%
681 133
 
1.0%
410 133
 
1.0%
Other values (136) 11349
89.5%
ValueCountFrequency (%)
3 102
0.8%
4 17
 
0.1%
5 133
1.0%
10 17
 
0.1%
11 133
1.0%
12 133
1.0%
13 133
1.0%
15 119
0.9%
16 119
0.9%
18 119
0.9%
ValueCountFrequency (%)
23826 85
0.7%
16795 95
0.7%
14171 19
 
0.1%
12321 38
 
0.3%
8970 38
 
0.3%
6574 133
1.0%
6195 133
1.0%
5358 38
 
0.3%
4795 34
 
0.3%
4171 38
 
0.3%

result_away
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.1 KiB
L
5654 
W
3854 
D
3171 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12679
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowL
2nd rowW
3rd rowD
4th rowL
5th rowW

Common Values

ValueCountFrequency (%)
L 5654
44.6%
W 3854
30.4%
D 3171
25.0%

Length

2023-02-23T20:08:13.119932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-23T20:08:13.204951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
l 5654
44.6%
w 3854
30.4%
d 3171
25.0%

Most occurring characters

ValueCountFrequency (%)
L 5654
44.6%
W 3854
30.4%
D 3171
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12679
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 5654
44.6%
W 3854
30.4%
D 3171
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12679
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 5654
44.6%
W 3854
30.4%
D 3171
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 5654
44.6%
W 3854
30.4%
D 3171
25.0%

result_home
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.1 KiB
W
5654 
L
3854 
D
3171 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters12679
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowL
3rd rowD
4th rowW
5th rowL

Common Values

ValueCountFrequency (%)
W 5654
44.6%
L 3854
30.4%
D 3171
25.0%

Length

2023-02-23T20:08:13.279968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-23T20:08:13.368995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
w 5654
44.6%
l 3854
30.4%
d 3171
25.0%

Most occurring characters

ValueCountFrequency (%)
W 5654
44.6%
L 3854
30.4%
D 3171
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12679
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 5654
44.6%
L 3854
30.4%
D 3171
25.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12679
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 5654
44.6%
L 3854
30.4%
D 3171
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 5654
44.6%
L 3854
30.4%
D 3171
25.0%

liga
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.1 KiB
Premier League
2660 
Serie A
2660 
La Liga
2660 
Ligue 1
2557 
Bundesliga
2142 

Length

Max length14
Median length7
Mean length8.9753924
Min length7

Characters and Unicode

Total characters113799
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPremier League
2nd rowPremier League
3rd rowPremier League
4th rowPremier League
5th rowPremier League

Common Values

ValueCountFrequency (%)
Premier League 2660
21.0%
Serie A 2660
21.0%
La Liga 2660
21.0%
Ligue 1 2557
20.2%
Bundesliga 2142
16.9%

Length

2023-02-23T20:08:13.450014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-23T20:08:13.552036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
premier 2660
11.5%
league 2660
11.5%
serie 2660
11.5%
a 2660
11.5%
la 2660
11.5%
liga 2660
11.5%
ligue 2557
11.0%
1 2557
11.0%
bundesliga 2142
9.2%

Most occurring characters

ValueCountFrequency (%)
e 20659
18.2%
i 12679
11.1%
10537
9.3%
L 10537
9.3%
a 10122
8.9%
g 10019
8.8%
r 7980
 
7.0%
u 7359
 
6.5%
S 2660
 
2.3%
A 2660
 
2.3%
Other values (8) 18587
16.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 80046
70.3%
Uppercase Letter 20659
 
18.2%
Space Separator 10537
 
9.3%
Decimal Number 2557
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20659
25.8%
i 12679
15.8%
a 10122
12.6%
g 10019
12.5%
r 7980
 
10.0%
u 7359
 
9.2%
m 2660
 
3.3%
n 2142
 
2.7%
d 2142
 
2.7%
s 2142
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
L 10537
51.0%
S 2660
 
12.9%
A 2660
 
12.9%
P 2660
 
12.9%
B 2142
 
10.4%
Space Separator
ValueCountFrequency (%)
10537
100.0%
Decimal Number
ValueCountFrequency (%)
1 2557
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100705
88.5%
Common 13094
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20659
20.5%
i 12679
12.6%
L 10537
10.5%
a 10122
10.1%
g 10019
9.9%
r 7980
 
7.9%
u 7359
 
7.3%
S 2660
 
2.6%
A 2660
 
2.6%
P 2660
 
2.6%
Other values (6) 13370
13.3%
Common
ValueCountFrequency (%)
10537
80.5%
1 2557
 
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113799
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 20659
18.2%
i 12679
11.1%
10537
9.3%
L 10537
9.3%
a 10122
8.9%
g 10019
8.8%
r 7980
 
7.0%
u 7359
 
6.5%
S 2660
 
2.3%
A 2660
 
2.3%
Other values (8) 18587
16.3%

home_name
Categorical

Distinct146
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size198.1 KiB
Manchester United
 
133
Sampdoria
 
133
Eibar
 
133
Real Madrid
 
133
Celta Vigo
 
133
Other values (141)
12014 

Length

Max length23
Median length19
Mean length9.3648553
Min length4

Characters and Unicode

Total characters118737
Distinct characters58
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManchester United
2nd rowBournemouth
3rd rowEverton
4th rowLeicester
5th rowNorwich

Common Values

ValueCountFrequency (%)
Manchester United 133
 
1.0%
Sampdoria 133
 
1.0%
Eibar 133
 
1.0%
Real Madrid 133
 
1.0%
Celta Vigo 133
 
1.0%
Real Sociedad 133
 
1.0%
Villarreal 133
 
1.0%
Athletic Club 133
 
1.0%
Lazio 133
 
1.0%
Juventus 133
 
1.0%
Other values (136) 11349
89.5%

Length

2023-02-23T20:08:13.654064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
real 437
 
2.5%
united 285
 
1.7%
manchester 266
 
1.5%
madrid 266
 
1.5%
borussia 238
 
1.4%
west 228
 
1.3%
berlin 153
 
0.9%
arsenal 133
 
0.8%
atalanta 133
 
0.8%
sevilla 133
 
0.8%
Other values (178) 14905
86.8%

Most occurring characters

ValueCountFrequency (%)
e 12105
 
10.2%
a 11922
 
10.0%
n 8273
 
7.0%
r 7782
 
6.6%
i 7487
 
6.3%
o 7380
 
6.2%
l 6854
 
5.8%
t 6468
 
5.4%
s 5053
 
4.3%
4498
 
3.8%
Other values (48) 40915
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95087
80.1%
Uppercase Letter 17761
 
15.0%
Space Separator 4498
 
3.8%
Decimal Number 1144
 
1.0%
Dash Punctuation 128
 
0.1%
Other Punctuation 119
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12105
12.7%
a 11922
12.5%
n 8273
8.7%
r 7782
8.2%
i 7487
7.9%
o 7380
 
7.8%
l 6854
 
7.2%
t 6468
 
6.8%
s 5053
 
5.3%
u 3268
 
3.4%
Other values (15) 18495
19.5%
Uppercase Letter
ValueCountFrequency (%)
B 1949
11.0%
S 1718
 
9.7%
C 1684
 
9.5%
M 1610
 
9.1%
L 1391
 
7.8%
A 1324
 
7.5%
R 949
 
5.3%
V 837
 
4.7%
G 798
 
4.5%
E 760
 
4.3%
Other values (12) 4741
26.7%
Decimal Number
ValueCountFrequency (%)
0 295
25.8%
1 209
18.3%
9 144
12.6%
3 133
11.6%
4 119
10.4%
5 119
10.4%
6 68
 
5.9%
2 57
 
5.0%
Space Separator
ValueCountFrequency (%)
4498
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%
Other Punctuation
ValueCountFrequency (%)
. 119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112848
95.0%
Common 5889
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12105
 
10.7%
a 11922
 
10.6%
n 8273
 
7.3%
r 7782
 
6.9%
i 7487
 
6.6%
o 7380
 
6.5%
l 6854
 
6.1%
t 6468
 
5.7%
s 5053
 
4.5%
u 3268
 
2.9%
Other values (37) 36256
32.1%
Common
ValueCountFrequency (%)
4498
76.4%
0 295
 
5.0%
1 209
 
3.5%
9 144
 
2.4%
3 133
 
2.3%
- 128
 
2.2%
. 119
 
2.0%
4 119
 
2.0%
5 119
 
2.0%
6 68
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12105
 
10.2%
a 11922
 
10.0%
n 8273
 
7.0%
r 7782
 
6.6%
i 7487
 
6.3%
o 7380
 
6.2%
l 6854
 
5.8%
t 6468
 
5.4%
s 5053
 
4.3%
4498
 
3.8%
Other values (48) 40915
34.5%

away_name
Categorical

Distinct146
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size198.1 KiB
Tottenham
 
133
Sevilla
 
133
Eibar
 
133
Villarreal
 
133
Celta Vigo
 
133
Other values (141)
12014 

Length

Max length23
Median length19
Mean length9.3648553
Min length4

Characters and Unicode

Total characters118737
Distinct characters58
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTottenham
2nd rowAston Villa
3rd rowWatford
4th rowSunderland
5th rowCrystal Palace

Common Values

ValueCountFrequency (%)
Tottenham 133
 
1.0%
Sevilla 133
 
1.0%
Eibar 133
 
1.0%
Villarreal 133
 
1.0%
Celta Vigo 133
 
1.0%
Barcelona 133
 
1.0%
Valencia 133
 
1.0%
Roma 133
 
1.0%
Real Sociedad 133
 
1.0%
Udinese 133
 
1.0%
Other values (136) 11349
89.5%

Length

2023-02-23T20:08:13.767090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
real 437
 
2.5%
united 285
 
1.7%
manchester 266
 
1.5%
madrid 266
 
1.5%
borussia 238
 
1.4%
west 228
 
1.3%
berlin 153
 
0.9%
tottenham 133
 
0.8%
athletic 133
 
0.8%
club 133
 
0.8%
Other values (178) 14904
86.8%

Most occurring characters

ValueCountFrequency (%)
e 12104
 
10.2%
a 11923
 
10.0%
n 8271
 
7.0%
r 7782
 
6.6%
i 7481
 
6.3%
o 7386
 
6.2%
l 6851
 
5.8%
t 6469
 
5.4%
s 5053
 
4.3%
4497
 
3.8%
Other values (48) 40920
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95088
80.1%
Uppercase Letter 17761
 
15.0%
Space Separator 4497
 
3.8%
Decimal Number 1144
 
1.0%
Dash Punctuation 128
 
0.1%
Other Punctuation 119
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12104
12.7%
a 11923
12.5%
n 8271
8.7%
r 7782
8.2%
i 7481
7.9%
o 7386
 
7.8%
l 6851
 
7.2%
t 6469
 
6.8%
s 5053
 
5.3%
u 3273
 
3.4%
Other values (15) 18495
19.5%
Uppercase Letter
ValueCountFrequency (%)
B 1952
11.0%
S 1719
 
9.7%
C 1686
 
9.5%
M 1610
 
9.1%
L 1390
 
7.8%
A 1322
 
7.4%
R 949
 
5.3%
V 837
 
4.7%
G 797
 
4.5%
E 760
 
4.3%
Other values (12) 4739
26.7%
Decimal Number
ValueCountFrequency (%)
0 295
25.8%
1 209
18.3%
9 144
12.6%
3 133
11.6%
4 119
10.4%
5 119
10.4%
6 68
 
5.9%
2 57
 
5.0%
Space Separator
ValueCountFrequency (%)
4497
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%
Other Punctuation
ValueCountFrequency (%)
. 119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 112849
95.0%
Common 5888
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12104
 
10.7%
a 11923
 
10.6%
n 8271
 
7.3%
r 7782
 
6.9%
i 7481
 
6.6%
o 7386
 
6.5%
l 6851
 
6.1%
t 6469
 
5.7%
s 5053
 
4.5%
u 3273
 
2.9%
Other values (37) 36256
32.1%
Common
ValueCountFrequency (%)
4497
76.4%
0 295
 
5.0%
1 209
 
3.5%
9 144
 
2.4%
3 133
 
2.3%
- 128
 
2.2%
4 119
 
2.0%
. 119
 
2.0%
5 119
 
2.0%
6 68
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12104
 
10.2%
a 11923
 
10.0%
n 8271
 
7.0%
r 7782
 
6.6%
i 7481
 
6.3%
o 7386
 
6.2%
l 6851
 
5.8%
t 6469
 
5.4%
s 5053
 
4.3%
4497
 
3.8%
Other values (48) 40920
34.5%

xGoals_home
Real number (ℝ)

Distinct12490
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5023134
Minimum0
Maximum6.63049
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:13.880118image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3494032
Q10.8370005
median1.34375
Q32.007645
95-th percentile3.154152
Maximum6.63049
Range6.63049
Interquartile range (IQR)1.1706445

Descriptive statistics

Standard deviation0.89171186
Coefficient of variation (CV)0.59355915
Kurtosis1.6390449
Mean1.5023134
Median Absolute Deviation (MAD)0.568807
Skewness1.0578785
Sum19047.831
Variance0.79515004
MonotonicityNot monotonic
2023-02-23T20:08:13.983141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.51059 3
 
< 0.1%
1.76818 3
 
< 0.1%
1.43229 2
 
< 0.1%
1.3355 2
 
< 0.1%
2.09938 2
 
< 0.1%
2.06277 2
 
< 0.1%
1.23419 2
 
< 0.1%
1.52646 2
 
< 0.1%
2.00977 2
 
< 0.1%
2.09891 2
 
< 0.1%
Other values (12480) 12657
99.8%
ValueCountFrequency (%)
0 2
< 0.1%
0.0128508 1
< 0.1%
0.0254617 1
< 0.1%
0.0268116 1
< 0.1%
0.0312154 1
< 0.1%
0.0315577 1
< 0.1%
0.0319694 1
< 0.1%
0.0388442 1
< 0.1%
0.0489155 1
< 0.1%
0.049118 1
< 0.1%
ValueCountFrequency (%)
6.63049 1
< 0.1%
6.61091 1
< 0.1%
6.56908 1
< 0.1%
6.46701 1
< 0.1%
6.37227 1
< 0.1%
6.1271 1
< 0.1%
6.0418 1
< 0.1%
6.02931 1
< 0.1%
5.85948 1
< 0.1%
5.85121 1
< 0.1%

shots_home
Real number (ℝ)

Distinct42
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.707154
Minimum0
Maximum47
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:14.086164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median13
Q317
95-th percentile23
Maximum47
Range47
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2646003
Coefficient of variation (CV)0.38407685
Kurtosis0.66789996
Mean13.707154
Median Absolute Deviation (MAD)3
Skewness0.62276821
Sum173793
Variance27.716017
MonotonicityNot monotonic
2023-02-23T20:08:14.186187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
14 1019
 
8.0%
11 999
 
7.9%
12 979
 
7.7%
13 970
 
7.7%
10 883
 
7.0%
15 851
 
6.7%
9 806
 
6.4%
16 803
 
6.3%
8 654
 
5.2%
17 647
 
5.1%
Other values (32) 4068
32.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 9
 
0.1%
2 21
 
0.2%
3 66
 
0.5%
4 117
 
0.9%
5 227
 
1.8%
6 376
3.0%
7 501
4.0%
8 654
5.2%
9 806
6.4%
ValueCountFrequency (%)
47 1
 
< 0.1%
43 1
 
< 0.1%
39 1
 
< 0.1%
38 2
 
< 0.1%
37 3
 
< 0.1%
36 4
 
< 0.1%
35 1
 
< 0.1%
34 6
< 0.1%
33 12
0.1%
32 10
0.1%

shotsOnTarget_home
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7427242
Minimum0
Maximum18
Zeros228
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:14.273206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5783933
Coefficient of variation (CV)0.54365239
Kurtosis0.62557513
Mean4.7427242
Median Absolute Deviation (MAD)2
Skewness0.72254969
Sum60133
Variance6.6481122
MonotonicityNot monotonic
2023-02-23T20:08:14.353225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4 2033
16.0%
3 2011
15.9%
5 1860
14.7%
6 1477
11.6%
2 1469
11.6%
7 1071
8.4%
1 756
 
6.0%
8 680
 
5.4%
9 469
 
3.7%
10 274
 
2.2%
Other values (9) 579
 
4.6%
ValueCountFrequency (%)
0 228
 
1.8%
1 756
 
6.0%
2 1469
11.6%
3 2011
15.9%
4 2033
16.0%
5 1860
14.7%
6 1477
11.6%
7 1071
8.4%
8 680
 
5.4%
9 469
 
3.7%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 2
 
< 0.1%
16 4
 
< 0.1%
15 10
 
0.1%
14 30
 
0.2%
13 44
 
0.3%
12 96
 
0.8%
11 164
 
1.3%
10 274
2.2%
9 469
3.7%

deep_home
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4245603
Minimum0
Maximum42
Zeros230
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:14.449248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q39
95-th percentile14.1
Maximum42
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.2762841
Coefficient of variation (CV)0.66561507
Kurtosis3.1372821
Mean6.4245603
Median Absolute Deviation (MAD)3
Skewness1.3585005
Sum81457
Variance18.286606
MonotonicityNot monotonic
2023-02-23T20:08:14.541269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4 1461
11.5%
5 1417
11.2%
6 1300
10.3%
3 1298
10.2%
7 1135
9.0%
2 1107
8.7%
8 884
7.0%
9 748
 
5.9%
1 676
 
5.3%
10 538
 
4.2%
Other values (28) 2115
16.7%
ValueCountFrequency (%)
0 230
 
1.8%
1 676
5.3%
2 1107
8.7%
3 1298
10.2%
4 1461
11.5%
5 1417
11.2%
6 1300
10.3%
7 1135
9.0%
8 884
7.0%
9 748
5.9%
ValueCountFrequency (%)
42 1
 
< 0.1%
37 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 2
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 4
< 0.1%

ppda_home
Real number (ℝ)

Distinct5172
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.496327
Minimum1.8974
Maximum97.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:14.644296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.8974
5-th percentile4.48713
Q16.8148
median9.25
Q312.61415
95-th percentile20.66845
Maximum97.3333
Range95.4359
Interquartile range (IQR)5.79935

Descriptive statistics

Standard deviation5.6093423
Coefficient of variation (CV)0.53441003
Kurtosis15.803202
Mean10.496327
Median Absolute Deviation (MAD)2.75
Skewness2.602442
Sum133082.93
Variance31.464721
MonotonicityNot monotonic
2023-02-23T20:08:14.748319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 54
 
0.4%
10 52
 
0.4%
11 47
 
0.4%
6 47
 
0.4%
7 46
 
0.4%
9 40
 
0.3%
12 37
 
0.3%
10.5 36
 
0.3%
13 33
 
0.3%
5 30
 
0.2%
Other values (5162) 12257
96.7%
ValueCountFrequency (%)
1.8974 1
< 0.1%
2.1143 1
< 0.1%
2.1935 1
< 0.1%
2.2432 1
< 0.1%
2.25 1
< 0.1%
2.2581 1
< 0.1%
2.2692 1
< 0.1%
2.2759 1
< 0.1%
2.3125 1
< 0.1%
2.3478 1
< 0.1%
ValueCountFrequency (%)
97.3333 1
< 0.1%
83.2 1
< 0.1%
67.5 1
< 0.1%
67.3333 1
< 0.1%
65.8571 1
< 0.1%
58.8 1
< 0.1%
56.75 1
< 0.1%
53.6667 1
< 0.1%
52.2857 1
< 0.1%
52 1
< 0.1%

corners_home
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4879722
Minimum0
Maximum20
Zeros187
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:14.840339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q37
95-th percentile11
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9558181
Coefficient of variation (CV)0.53859931
Kurtosis0.61432727
Mean5.4879722
Median Absolute Deviation (MAD)2
Skewness0.69822552
Sum69582
Variance8.7368604
MonotonicityNot monotonic
2023-02-23T20:08:14.928359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 1778
14.0%
5 1758
13.9%
3 1544
12.2%
6 1533
12.1%
7 1265
10.0%
2 1118
8.8%
8 983
7.8%
9 678
 
5.3%
1 594
 
4.7%
10 489
 
3.9%
Other values (11) 939
7.4%
ValueCountFrequency (%)
0 187
 
1.5%
1 594
 
4.7%
2 1118
8.8%
3 1544
12.2%
4 1778
14.0%
5 1758
13.9%
6 1533
12.1%
7 1265
10.0%
8 983
7.8%
9 678
 
5.3%
ValueCountFrequency (%)
20 3
 
< 0.1%
19 3
 
< 0.1%
18 9
 
0.1%
17 11
 
0.1%
16 19
 
0.1%
15 33
 
0.3%
14 65
 
0.5%
13 133
1.0%
12 169
1.3%
11 307
2.4%

xGoals_away
Real number (ℝ)

Distinct12511
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1787008
Minimum0
Maximum6.18696
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:15.034384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2213234
Q10.593545
median1.03212
Q31.59803
95-th percentile2.652263
Maximum6.18696
Range6.18696
Interquartile range (IQR)1.004485

Descriptive statistics

Standard deviation0.77657464
Coefficient of variation (CV)0.65883951
Kurtosis1.7082042
Mean1.1787008
Median Absolute Deviation (MAD)0.48809
Skewness1.1266894
Sum14944.747
Variance0.60306817
MonotonicityNot monotonic
2023-02-23T20:08:15.133405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.1%
1.39172 3
 
< 0.1%
1.04957 3
 
< 0.1%
1.58437 3
 
< 0.1%
1.28316 2
 
< 0.1%
1.34566 2
 
< 0.1%
1.61764 2
 
< 0.1%
1.13378 2
 
< 0.1%
0.326909 2
 
< 0.1%
1.68478 2
 
< 0.1%
Other values (12501) 12650
99.8%
ValueCountFrequency (%)
0 8
0.1%
0.00671621 1
 
< 0.1%
0.0127481 1
 
< 0.1%
0.0131321 1
 
< 0.1%
0.0209842 1
 
< 0.1%
0.0239835 1
 
< 0.1%
0.0254182 1
 
< 0.1%
0.0263437 1
 
< 0.1%
0.0263448 1
 
< 0.1%
0.0270339 1
 
< 0.1%
ValueCountFrequency (%)
6.18696 1
< 0.1%
5.94023 1
< 0.1%
5.76112 1
< 0.1%
5.51767 1
< 0.1%
5.40731 1
< 0.1%
5.34676 1
< 0.1%
5.2681 1
< 0.1%
5.2489 1
< 0.1%
5.0778 1
< 0.1%
5.06788 1
< 0.1%

shots_away
Real number (ℝ)

Distinct35
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.21721
Minimum0
Maximum39
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:15.231427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median11
Q314
95-th percentile20
Maximum39
Range39
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6566256
Coefficient of variation (CV)0.41513226
Kurtosis0.48573574
Mean11.21721
Median Absolute Deviation (MAD)3
Skewness0.6003547
Sum142223
Variance21.684162
MonotonicityNot monotonic
2023-02-23T20:08:15.324448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
9 1170
 
9.2%
10 1105
 
8.7%
11 1037
 
8.2%
8 998
 
7.9%
12 983
 
7.8%
7 907
 
7.2%
13 905
 
7.1%
14 831
 
6.6%
6 754
 
5.9%
15 651
 
5.1%
Other values (25) 3338
26.3%
ValueCountFrequency (%)
0 8
 
0.1%
1 27
 
0.2%
2 75
 
0.6%
3 203
 
1.6%
4 328
 
2.6%
5 522
4.1%
6 754
5.9%
7 907
7.2%
8 998
7.9%
9 1170
9.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
34 2
 
< 0.1%
33 1
 
< 0.1%
31 2
 
< 0.1%
30 7
 
0.1%
29 2
 
< 0.1%
28 7
 
0.1%
27 15
 
0.1%
26 30
0.2%
25 39
0.3%

shotsOnTarget_away
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.892736
Minimum0
Maximum15
Zeros431
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:15.414468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile8
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2816393
Coefficient of variation (CV)0.58612742
Kurtosis0.71773842
Mean3.892736
Median Absolute Deviation (MAD)2
Skewness0.74549972
Sum49356
Variance5.205878
MonotonicityNot monotonic
2023-02-23T20:08:15.486485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 2333
18.4%
4 2114
16.7%
2 2080
16.4%
5 1627
12.8%
1 1277
10.1%
6 1191
9.4%
7 738
 
5.8%
0 431
 
3.4%
8 423
 
3.3%
9 232
 
1.8%
Other values (6) 233
 
1.8%
ValueCountFrequency (%)
0 431
 
3.4%
1 1277
10.1%
2 2080
16.4%
3 2333
18.4%
4 2114
16.7%
5 1627
12.8%
6 1191
9.4%
7 738
 
5.8%
8 423
 
3.3%
9 232
 
1.8%
ValueCountFrequency (%)
15 4
 
< 0.1%
14 8
 
0.1%
13 15
 
0.1%
12 27
 
0.2%
11 64
 
0.5%
10 115
 
0.9%
9 232
 
1.8%
8 423
 
3.3%
7 738
5.8%
6 1191
9.4%

deep_away
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2171307
Minimum0
Maximum28
Zeros475
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:15.572504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q37
95-th percentile12
Maximum28
Range28
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.7138884
Coefficient of variation (CV)0.71186417
Kurtosis2.4312063
Mean5.2171307
Median Absolute Deviation (MAD)2
Skewness1.2827248
Sum66148
Variance13.792967
MonotonicityNot monotonic
2023-02-23T20:08:15.652522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 1656
13.1%
4 1624
12.8%
2 1520
12.0%
5 1410
11.1%
6 1172
9.2%
1 1117
8.8%
7 915
7.2%
8 725
5.7%
9 509
 
4.0%
0 475
 
3.7%
Other values (19) 1556
12.3%
ValueCountFrequency (%)
0 475
 
3.7%
1 1117
8.8%
2 1520
12.0%
3 1656
13.1%
4 1624
12.8%
5 1410
11.1%
6 1172
9.2%
7 915
7.2%
8 725
5.7%
9 509
 
4.0%
ValueCountFrequency (%)
28 2
 
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 2
 
< 0.1%
24 5
 
< 0.1%
23 7
 
0.1%
22 9
 
0.1%
21 11
 
0.1%
20 19
0.1%
19 29
0.2%

ppda_away
Real number (ℝ)

Distinct5202
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.835413
Minimum2.122
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:15.750544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.122
5-th percentile4.794
Q17.48335
median10.2286
Q314.1914
95-th percentile24
Maximum152
Range149.878
Interquartile range (IQR)6.70805

Descriptive statistics

Standard deviation6.9255202
Coefficient of variation (CV)0.5851524
Kurtosis27.870005
Mean11.835413
Median Absolute Deviation (MAD)3.1881
Skewness3.2775277
Sum150061.2
Variance47.962829
MonotonicityNot monotonic
2023-02-23T20:08:15.851567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 56
 
0.4%
9 50
 
0.4%
11 48
 
0.4%
8 47
 
0.4%
6 44
 
0.3%
13 40
 
0.3%
7 40
 
0.3%
12 36
 
0.3%
15 31
 
0.2%
14 30
 
0.2%
Other values (5192) 12257
96.7%
ValueCountFrequency (%)
2.122 1
< 0.1%
2.3684 1
< 0.1%
2.3889 1
< 0.1%
2.3929 1
< 0.1%
2.4138 1
< 0.1%
2.4211 1
< 0.1%
2.4412 1
< 0.1%
2.4483 1
< 0.1%
2.5 1
< 0.1%
2.5294 1
< 0.1%
ValueCountFrequency (%)
152 1
< 0.1%
91.8 1
< 0.1%
90.3333 1
< 0.1%
89.5 1
< 0.1%
82.8571 1
< 0.1%
82.6667 1
< 0.1%
82.2 1
< 0.1%
81.2857 1
< 0.1%
79.3333 1
< 0.1%
79.1667 1
< 0.1%

corners_away
Real number (ℝ)

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.468018
Minimum0
Maximum19
Zeros356
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size198.1 KiB
2023-02-23T20:08:15.946589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6034486
Coefficient of variation (CV)0.58268534
Kurtosis0.55336653
Mean4.468018
Median Absolute Deviation (MAD)2
Skewness0.70074213
Sum56650
Variance6.7779446
MonotonicityNot monotonic
2023-02-23T20:08:16.023606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 1987
15.7%
3 1952
15.4%
5 1723
13.6%
2 1594
12.6%
6 1345
10.6%
1 1121
8.8%
7 1016
8.0%
8 633
 
5.0%
9 417
 
3.3%
0 356
 
2.8%
Other values (10) 535
 
4.2%
ValueCountFrequency (%)
0 356
 
2.8%
1 1121
8.8%
2 1594
12.6%
3 1952
15.4%
4 1987
15.7%
5 1723
13.6%
6 1345
10.6%
7 1016
8.0%
8 633
 
5.0%
9 417
 
3.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 3
 
< 0.1%
15 6
 
< 0.1%
14 22
 
0.2%
13 42
 
0.3%
12 74
 
0.6%
11 132
1.0%
10 252
2.0%

Interactions

2023-02-23T20:08:08.721452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:30.384823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:32.469797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:34.566133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:36.626596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.714437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.664445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:42.578391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:44.550011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:46.493448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:48.480895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:50.397326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:52.472794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:54.389224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:56.295654image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:58.411129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:00.413580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:02.592069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:04.600521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:06.572965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:08.829476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:30.496848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:32.578825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:34.677158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:36.737621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.818460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.762467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:42.683327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:44.653034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:46.600472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:48.583919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:50.507351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:52.574816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:54.490247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:56.405678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:58.519153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:00.525604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:02.700094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:04.704545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:06.696993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-02-23T20:07:31.630103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:33.738947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:35.785407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:37.873892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:39.870379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:41.799518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:43.743829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:45.699269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:47.677714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:49.620152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:51.646607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:53.610049image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:55.522479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:57.538933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:59.599397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:01.749880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:03.787338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:05.752780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:07.863255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:10.068754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:31.729126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:33.836969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:35.883429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:37.976915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:39.961400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:41.891439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:43.838851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:45.792290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:47.770736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:49.711172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:51.742629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:53.698068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:55.610499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:57.641956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:59.697418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:01.858905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:03.881359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:05.846802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:07.976280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:10.181779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:31.839150image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:33.945994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:35.991453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.086792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.068424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:41.994383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:43.942874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:45.897314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:47.877760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:49.815195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:51.848652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:53.797091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:55.712522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:57.752981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:59.803443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:01.974930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:03.989384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:05.956826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:08.096308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:10.289804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:31.941678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:34.046016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:36.095477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.189819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.164446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:42.091388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:44.039896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:45.994335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:47.973781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:49.910216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:51.946676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:53.892112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:55.808543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:57.856004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:59.904465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:02.075953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:04.087406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:06.057850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:08.201331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:10.398828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:32.050703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:34.153040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:36.206501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.295843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.268395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:42.191410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:44.145920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:46.099359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:48.077804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:50.014240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:52.058700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:53.989134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:55.910566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:57.983033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:00.012490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:02.181977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:04.193430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:06.163873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:08.308358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:10.507853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:32.156727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:34.258064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:36.313526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.401866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.368378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:42.289432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:44.249943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:46.200382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:48.178827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:50.109262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:52.169725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:54.086156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:56.008588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:58.093058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:00.115512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:02.289001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:04.294452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:06.263896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:08.414382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:10.604875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:32.257750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:34.356085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:36.414548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.501893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.462399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:42.381453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:44.343964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:46.295403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:48.275849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:50.200282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:52.265746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:54.176176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:56.098608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:58.195080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:00.208533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:02.384022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:04.392474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:06.354916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:08.513404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:10.714900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:32.363775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:34.458109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:36.518572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:38.607413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:40.560421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:42.478480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:44.443987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:46.392426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:48.379872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:50.297303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:52.368770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:54.282200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:56.196631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:07:58.301105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:00.308557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:02.487046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:04.497498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:06.460940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-23T20:08:08.614427image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-23T20:08:16.131633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
gameIDseasonhomeTeamIDawayTeamIDhomeGoalsawayGoalshome_club_idaway_club_idxGoals_homeshots_homeshotsOnTarget_homedeep_homeppda_homecorners_homexGoals_awayshots_awayshotsOnTarget_awaydeep_awayppda_awaycorners_awayresult_awayresult_homeliga
gameID1.0000.8780.2100.210-0.0090.0450.0010.0000.012-0.043-0.0250.0030.168-0.0550.0770.0060.0100.0460.143-0.0010.0330.0330.292
season0.8781.0000.1050.106-0.0040.0510.0020.0010.016-0.046-0.0230.0210.190-0.0580.0890.0110.0180.0700.1570.0070.0320.0320.000
homeTeamID0.2100.1051.0000.639-0.0690.0120.2020.109-0.076-0.109-0.082-0.173-0.001-0.0860.003-0.0250.011-0.074-0.065-0.0410.0860.0860.774
awayTeamID0.2100.1060.6391.0000.034-0.0720.1100.2020.040-0.0050.026-0.063-0.083-0.009-0.099-0.117-0.086-0.2020.042-0.0980.0710.0710.774
homeGoals-0.009-0.004-0.0690.0341.000-0.076-0.1140.0520.6080.2640.5670.244-0.0170.012-0.133-0.076-0.096-0.0980.125-0.0230.4630.4630.018
awayGoals0.0450.0510.012-0.072-0.0761.0000.043-0.102-0.125-0.063-0.094-0.0650.082-0.0420.6070.3020.5630.251-0.0530.0370.4890.4890.034
home_club_id0.0010.0020.2020.110-0.1140.0431.0000.196-0.138-0.114-0.123-0.1710.048-0.0750.0360.0200.0240.026-0.1410.0390.0580.0580.207
away_club_id0.0000.0010.1090.2020.052-0.1020.1961.0000.0630.0430.0340.044-0.1460.081-0.141-0.118-0.130-0.1720.073-0.0700.0680.0680.207
xGoals_home0.0120.016-0.0760.0400.608-0.125-0.1380.0631.0000.6160.6600.476-0.1770.254-0.192-0.195-0.160-0.2010.257-0.1480.3200.3200.031
shots_home-0.043-0.046-0.109-0.0050.264-0.063-0.1140.0430.6161.0000.6430.554-0.3150.502-0.201-0.301-0.181-0.2840.371-0.2630.1260.1260.059
shotsOnTarget_home-0.025-0.023-0.0820.0260.567-0.094-0.1230.0340.6600.6431.0000.418-0.1730.285-0.173-0.195-0.143-0.1940.261-0.1490.2900.2900.034
deep_home0.0030.021-0.173-0.0630.244-0.065-0.1710.0440.4760.5540.4181.000-0.2890.368-0.167-0.249-0.151-0.2310.399-0.2160.1240.1240.075
ppda_home0.1680.190-0.001-0.083-0.0170.0820.048-0.146-0.177-0.315-0.173-0.2891.000-0.3090.2130.3320.2050.370-0.1880.2450.0710.0710.058
corners_home-0.055-0.058-0.086-0.0090.012-0.042-0.0750.0810.2540.5020.2850.368-0.3091.000-0.171-0.270-0.157-0.2480.270-0.2350.0440.0440.040
xGoals_away0.0770.0890.003-0.099-0.1330.6070.036-0.141-0.192-0.201-0.173-0.1670.213-0.1711.0000.6310.6540.490-0.1840.2590.3290.3290.041
shots_away0.0060.011-0.025-0.117-0.0760.3020.020-0.118-0.195-0.301-0.195-0.2490.332-0.2700.6311.0000.6520.554-0.3070.4760.1530.1530.068
shotsOnTarget_away0.0100.0180.011-0.086-0.0960.5630.024-0.130-0.160-0.181-0.143-0.1510.205-0.1570.6540.6521.0000.420-0.1740.2690.2950.2950.045
deep_away0.0460.070-0.074-0.202-0.0980.2510.026-0.172-0.201-0.284-0.194-0.2310.370-0.2480.4900.5540.4201.000-0.3000.3430.1550.1550.080
ppda_away0.1430.157-0.0650.0420.125-0.053-0.1410.0730.2570.3710.2610.399-0.1880.270-0.184-0.307-0.174-0.3001.000-0.2880.0860.0860.051
corners_away-0.0010.007-0.041-0.098-0.0230.0370.039-0.070-0.148-0.263-0.149-0.2160.245-0.2350.2590.4760.2690.343-0.2881.0000.0180.0180.039
result_away0.0330.0320.0860.0710.4630.4890.0580.0680.3200.1260.2900.1240.0710.0440.3290.1530.2950.1550.0860.0181.0001.0000.009
result_home0.0330.0320.0860.0710.4630.4890.0580.0680.3200.1260.2900.1240.0710.0440.3290.1530.2950.1550.0860.0181.0001.0000.009
liga0.2920.0000.7740.7740.0180.0340.2070.2070.0310.0590.0340.0750.0580.0400.0410.0680.0450.0800.0510.0390.0090.0091.000

Missing values

2023-02-23T20:08:10.894940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-23T20:08:11.242018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

gameIDseasondatehomeTeamIDawayTeamIDhomeGoalsawayGoalshome_club_idaway_club_idresult_awayresult_homeligahome_nameaway_namexGoals_homeshots_homeshotsOnTarget_homedeep_homeppda_homecorners_homexGoals_awayshots_awayshotsOnTarget_awaydeep_awayppda_awaycorners_away
081.02015.02015-08-0889.082.01.00.0985148LWPremier LeagueManchester UnitedTottenham0.6275399.01.04.013.82611.00.6746009.04.010.08.21882.0
182.02015.02015-08-0873.071.00.01.0989405WLPremier LeagueBournemouthAston Villa0.87610611.02.011.06.90006.00.7822537.03.02.011.84623.0
283.02015.02015-08-0872.090.02.02.0291010DDPremier LeagueEvertonWatford0.60422610.05.05.06.65008.00.55789211.05.04.017.15792.0
384.02015.02015-08-0875.077.04.02.01003289LWPremier LeagueLeicesterSunderland2.56803019.08.05.010.88006.01.45946011.05.06.09.55563.0
485.02015.02015-08-0879.078.01.03.01123873WLPremier LeagueNorwichCrystal Palace1.13076017.06.05.05.73681.02.10975011.07.010.010.62504.0
586.02015.02015-08-0880.084.02.02.06312288DDPremier LeagueChelseaSwansea0.64396011.03.010.010.36364.02.59203017.010.05.08.83338.0
687.02015.02015-08-0986.074.02.02.0762180DDPremier LeagueNewcastle UnitedSouthampton1.5461309.04.03.012.72226.01.25290014.04.05.07.00006.0
788.02015.02015-08-0983.081.00.02.011379WLPremier LeagueArsenalWest Ham1.33166022.06.011.08.16675.00.5359618.04.00.012.45454.0
889.02015.02015-08-0985.087.00.01.051231WLPremier LeagueStokeLiverpool0.3812747.01.02.011.91303.00.3298738.03.05.09.34625.0
990.02015.02015-08-1076.088.00.03.0984281WLPremier LeagueWest Bromwich AlbionManchester City0.4352389.02.04.023.29416.01.92420020.07.08.08.08706.0
gameIDseasondatehomeTeamIDawayTeamIDhomeGoalsawayGoalshome_club_idaway_club_idresult_awayresult_homeligahome_nameaway_namexGoals_homeshots_homeshotsOnTarget_homedeep_homeppda_homecorners_homexGoals_awayshots_awayshotsOnTarget_awaydeep_awayppda_awaycorners_away
1266916126.02020.02021-05-23167.0160.01.02.014201082WLLigue 1AngersLille0.73695610.04.02.016.33334.01.5619205.02.00.015.92862.0
1267016127.02020.02021-05-23241.0161.00.02.03911583WLLigue 1BrestParis Saint Germain0.5747136.05.04.026.53855.02.0167009.04.06.011.50004.0
1267116128.02020.02021-05-23210.0171.00.00.0826162DDLigue 1LensMonaco0.2383904.01.02.024.76922.01.30800019.03.012.07.86967.0
1267216129.02020.02021-05-23178.0170.02.03.01041417WLLigue 1LyonNizza1.32809012.06.012.015.00006.01.47366014.06.06.016.11766.0
1267316130.02020.02021-05-23180.0164.01.01.0347244DDLigue 1MetzMarseille1.08944010.03.03.020.09521.01.1502209.02.03.09.047610.0
1267416131.02020.02021-05-23168.0166.01.02.0995969WLLigue 1NantesMontpellier1.41119015.05.017.012.36849.01.7075108.05.03.08.35295.0
1267516132.02020.02021-05-23177.0176.01.02.0142140WLLigue 1ReimsBordeaux1.19819010.03.03.016.26325.01.23805012.05.04.027.00002.0
1267616133.02020.02021-05-23163.0235.02.00.02731160LWLigue 1RennesNimes1.33269012.06.010.08.28574.00.3575839.02.00.039.72733.0
1267716134.02020.02021-05-23175.0181.00.01.06182969WLLigue 1Saint-EtienneDijon1.46050019.05.06.07.56009.01.38029010.02.03.014.72003.0
1267816135.02020.02021-05-23225.0179.01.01.06671158DDLigue 1StrasbourgLorient0.3239606.02.01.015.10002.00.5219137.01.00.015.95243.0